Monetizing content with AI: Going beyond traditional advertising to unlock new value

Broadcasters are turning to artificial intelligence and machine learning to unlock value from vast content libraries and develop new revenue streams as traditional advertising models evolve.

These AI systems, which analyze viewer behavior and automate content management, arrive as media companies face increasing pressure to monetize content across multiple platforms.

The technology aims to maximize revenue from existing content while adapting to fragmented viewing habits and shifting advertiser demands.

“AI is enabling broadcasters to optimize revenue generation beyond traditional advertising and subscription models,” said Zeenal Thakare, senior vice president of enterprise solutions architecture at Ateliere. “From personalizing ads to AI-generated content, AI is unlocking new monetization opportunities and commercial models.”

Maximizing content value through AI analysis

Content libraries represent significant untapped potential for many broadcasters. AI systems now identify and categorize this material at scale, enabling media companies to surface relevant content more efficiently.

“AI’s ability to efficiently and accurately search, tag and categorize content can be used to help surface content that closely aligns individual viewer preferences, and that may otherwise remain hidden,” said Stefan Lederer, CEO and co-founder of Bitmovin.

This automated content analysis extends beyond basic categorization. Broadcasters now use AI to identify opportunities for content repurposing, creating themed programming packages and anniversary specials from archived material without significant production costs.

The technology proves particularly valuable for free ad-supported streaming television (FAST) channels, where programming decisions directly impact advertising revenue. AI systems analyze viewing patterns across FAST channels to optimize scheduling and create thematic channels, enabling broadcasters to identify high-performing content and adjust strategies in response to viewer behavior.

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At the household level, AI processes multiple data points to refine content recommendations, marking a shift from broad demographic targeting to personalized experiences.

“By analyzing vast amounts of data, AI ensures viewers are presented with content they’re most likely to enjoy, keeping them engaged and reducing churn,” said Kathy Klinger, chief marketing officer at Brightcove.

AI-driven ad optimization

The impact of AI extends beyond content discovery to reshape advertising strategies. Current systems analyze content in real-time, enabling contextual ad placement that wasn’t possible with traditional methods.

“AI contextual advertising analyzes video and audio content to provide hyper-personalized ads for viewers based on the content they are watching, resulting in more ad-generated revenue,” Lederer said.

These systems also determine optimal ad timing by analyzing user engagement patterns. “If you combine AI contextual advertising with predictive analytics, it’s possible to predict user engagement and conversion rates at different points of the video so that the ad can be placed when the viewer is most likely to convert,” Lederer said.

The technology’s reach extends to inventory management and pricing. Dave Dembowski, senior vice president of global sales at Operative, said broadcasters use AI to optimize inventory allocation. “AI can help broadcasters know what to sell up front, at what price, and what inventory to hold back based on likely demand closer to delivery,” he said.

Revenue diversification through data insights

As viewing habits evolve, AI analysis gives broadcasters detailed insights into viewer behavior, enabling new revenue models beyond traditional advertising.

“Monetization strategies that will take front row seats with AI include content licensing and distribution optimization, sponsorship and brand integrations, targeted subscription and pay-per-view and bundle models, all driven by audience analytics, behavioral targeting and predictive analytics,” Thakare said.

Rights management, a traditionally labor-intensive process, now benefits from AI automation.

“With AI, broadcasters can automate many of the manual and time-consuming tasks involved in these processes such as contract analysis, monitoring content usage in real time to ensure rights are being enforced and analyzing data to detect potential breaches,” Lederer said.

Implementation challenges remain significant.

Yang Cai, CEO and president of VisualOn, cited “high implementation costs, the complexity of integrating AI with existing workflows, and a lack of technical expertise among staff” as primary barriers. Data privacy concerns and building trust in AI systems present additional hurdles.

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Success requires substantial investment in both technology and staff development. “Organizations should cultivate a culture of continuous learning, equipping teams with the skills to use AI tools effectively while understanding the ethical implications and regulatory frameworks that govern their use,” Klinger said.

As the broadcast industry evolves, AI tools enable media companies to develop monetization strategies that adapt to changing viewer behavior while maintaining advertising effectiveness and content value.

The technology’s impact extends across the broadcast ecosystem, from content discovery to ad placement, suggesting broader changes ahead for media monetization strategies.